4 research outputs found
Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces
To enable safe and efficient human-robot collaboration in shared workspaces
it is important for the robot to predict how a human will move when performing
a task. While predicting human motion for tasks not known a priori is very
challenging, we argue that single-arm reaching motions for known tasks in
collaborative settings (which are especially relevant for manufacturing) are
indeed predictable. Two hypotheses underlie our approach for predicting such
motions: First, that the trajectory the human performs is optimal with respect
to an unknown cost function, and second, that human adaptation to their
partner's motion can be captured well through iterative re-planning with the
above cost function. The key to our approach is thus to learn a cost function
which "explains" the motion of the human. To do this, we gather example
trajectories from pairs of participants performing a collaborative assembly
task using motion capture. We then use Inverse Optimal Control to learn a cost
function from these trajectories. Finally, we predict reaching motions from the
human's current configuration to a task-space goal region by iteratively
re-planning a trajectory using the learned cost function. Our planning
algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF
human kinematic model and accounts for the presence of a moving collaborator
and obstacles in the environment. Our results suggest that in most cases, our
method outperforms baseline methods when predicting motions. We also show that
our method outperforms baselines for predicting human motion when a human and a
robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201
Toward Enabling Safe & Efficient Human-Robot Manipulation in Shared Workspaces
When humans interact, there are many avenues of physical communication available ranging from vocal to physical gestures. In our past observations, when humans collaborate on manipulation tasks in shared workspaces there is often minimal to no verbal or physical communication, yet the collaboration is still fluid with minimal interferences between partners. However, when humans perform similar tasks in the presence of a robot collaborator, manipulation can be clumsy, disconnected, or simply not human-like. The focus of this work is to leverage our observations of human-human interaction in a robot's motion planner in order to facilitate more safe, efficient, and human-like collaborative manipulation in shared workspaces.
We first present an approach to formulating the cost function for a motion planner intended for human-robot collaboration such that robot motions are both safe and efficient. To achieve this, we propose two factors to consider in the cost function for the robot's motion planner: (1) Avoidance of the workspace previously-occupied by the human, so robot motion is safe as possible, and (2) Consistency of the robot's motion, so that the motion is predictable as possible for the human and they can perform their task without focusing undue attention on the robot. Our experiments in simulation and a human-robot workspace sharing study compare a cost function that uses only the first factor and a combined cost that uses both factors vs. a baseline method that is perfectly consistent but does not account for the human's previous motion. We find using either cost function we outperform the baseline method in terms of task success rate without degrading the task completion time. The best task success rate is achieved with the cost function that includes both the avoidance and consistency terms.
Next, we present an approach to human-attention aware robot motion generation which attempts to convey intent of the robot's task to its collaborator. We capture human attention through the combined use of a wearable eye-tracker and motion capture system. Since human attention isn't static, we present a method of generating a motion policy that can be queried online. Finally, we show preliminary tests of this method
Exploring Human-Robot interaction in Collaborative Tasks
In this project I performed independent research in the field of Human-Robot Collaboration. In doing so, I divided the project into two sub-problems: motion segmentation, and motion planning in the presence of a human. I present an effective method for automatic segmentation of human grasping motions as well as a novel cost function that aims to minimize robotic interference to a human collaborator's workspace
Quality Urban Living in Hong Kong: Shrinking Spaces
This project, prepared for the Urban Renewal Authority addresses the issue of a minimum dwelling size in metropolitan Hong Kong. Due to a steadily increasing population and relatively small developable land area, the region’s housing market is becoming increasingly unaffordable. In order to combat this phenomenon, our team found that a unit size of less than 300 square feet may provide an affordable yet comfortable option for those wishing to live in a Central Business District. To achieve a high quality of life in shrinking spaces, we suggest that future developments of this size employ intelligent layout design, space saving furniture, and multiple storage options while at the same time creating shared facilities that nurture community